Nature系列综述：AI智能体重塑癌症研究与治疗

Core Insights - The article discusses the rapid advancement of AI agents, particularly in cancer research and oncology, highlighting their capabilities beyond traditional AI systems [3][4][6] - AI agents can autonomously optimize drug design, propose treatment strategies, and handle complex multi-step problems that previous AI systems could not address [3][4][27] Group 1: AI Agents Overview - AI agents differ from traditional AI systems by possessing "action capabilities," allowing them to perceive their environment, plan multi-step tasks, and execute complex workflows with minimal human intervention [8][14] - The integration of large language models (LLMs) with external tools enables AI agents to actively gather information, analyze data, and take actions rather than merely responding to commands [14] Group 2: Applications in Cancer Research - AI agents can autonomously generate research hypotheses, design experimental protocols, execute data analysis, and write academic papers, marking a significant shift towards fully automated research processes [17][15] - Multi-agent collaborative systems are emerging, where different AI agents simulate human research teams by taking on specific expert roles, enhancing problem-solving comprehensiveness and decision-making transparency [18] Group 3: Clinical Oncology Applications - In clinical settings, AI agents can integrate various medical data sources, support treatment decisions, and automate clinical trial matching, significantly improving efficiency and patient outcomes [22][20] - AI agents are capable of simulating human expert reasoning in image analysis, allowing for more complex clinical problem-solving [23] Group 4: Future Outlook and Challenges - The article outlines a three-phase process of "agentification" in cancer research and oncology, predicting a transition from current AI interfaces to fully integrated systems with autonomous capabilities [28][29] - Challenges include the need for new evaluation metrics for AI agents' performance, integration hurdles from research prototypes to clinical tools, and ethical considerations regarding the autonomy of AI systems [27][29]